Training set training method and device

Abstract

The embodiment of the invention provides a training set training method and device. The method comprises: acquiring a training set used for training a preset model; filtering the interference sample pictures from all the sample pictures to construct the first training set, and reserving all the first residual sample pictures except the interference sample pictures; filtering out the first target picture from all the first residual sample pictures to construct the second training set, and reserving all the second residual sample pictures except the interference sample picture and the first target picture; obtaining the second target picture from all the second residual sample pictures to construct the third training set; and training the constructed first training set, second training set and third training set respectively. The device executes the method. According to the training set training method and device, the rationality of training set construction can be improved, and then thetraining set can be trained more reasonably.

Description

technical field [0001] Embodiments of the present invention relate to the technical field of image processing, and in particular, to a training method and device for a training set. Background technique [0002] Capsule endoscopy has the advantages of being painless, non-invasive, and the captured images contain a large amount of information, and has a wide range of application values. [0003] The existing technology uses artificial methods to identify and classify the original pictures taken by capsule endoscopy. In order to identify the original pictures more accurately and efficiently, it is necessary to build a model, but the model usually needs to be trained before it is used. training set so that the model can perform image recognition more accurately, however, the existing training methods for the training set, due to the unreasonable construction of the training set, the accuracy of the trained model is not high. [0004] Therefore, how to avoid the above defects, ...

AI Technical Summary

Problems solved by technology

[0003] The existing technology uses artificial methods to identify and classify the original pictures taken by capsule endoscopy. In order to identify the original pictures more accurately and efficiently, it is necessary to build a model, but the model usually needs to be trained before it is used. training set so that the model can perform image recognition more accurately, however, the existing training methods for the training set, because the training set constructed is not reasonable enough, resulting in low accuracy of the trained model

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